The library() function loads “packages” that perfrom special functions into the workspace.

library(dplyr)   
library(tigris)  
library(tmap)
options(tigris_class = "sf")  # Read shape files as Simiple Features objects

This step reads in a dataset from the cloud on votting patterns in Califonira. The dataset is from a California Open Data Set. The main portal home pages is here https://data.chhs.ca.gov/

voterData.t <- read.csv("https://data.chhs.ca.gov/dataset/40fd0792-2bfd-4303-a848-fc5cb4338295/resource/c384c86a-49d2-4128-8389-b2701ff0bc35/download/voter-registration-2002-2010.csv",as.is=TRUE)

This little step reads in a census-tract level geographic “shape file” for California from the US census. (A free “key” from the US Census in needed if you want to run it yourself; available here https://api.census.gov/data/key_signup.html. )

tracts_CA   <- tracts(state = "CA", cb = TRUE) 
voterData <- filter(voterData.t, geotype=="CT", 
                                 reportyear==2010, 
                                 race_eth_name=="Total", 
                                 type == "voted/registered") %>%
             transform(GEOID=paste0("0",geotypevalue))

This step merges the data using “join”

map.1    <- left_join(tracts_CA, voterData, by="GEOID")

This step makes a very simple map, with coloring based on the percent in the census tract who voted

tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(map.1) + tm_polygons(col="percent")
## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3

This step filter the data to just Conta Costa County, and re-maps

map.2 <- filter(map.1,county_name == "Contra Costa")

tm_shape(map.2) + tm_polygons(col="percent")